IRJET- Design and Implementation of Sentiment Analyzer for Top Engineering Colleges in India using Online Twitter Data

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 05 Issue: 11 | Nov 2018 www.irjet.net p-ISSN: 2395-0072
© 2018, IRJET | Impact Factor value: 7.211 | ISO 9001:2008 Certified Journal | Page 1594
Design and implementation of Sentiment Analyzer for top Engineering
colleges in India using online Twitter data
Neelam Shukla1
1Student, Department of Computer Science and Information Technology, Sam Higginbottom University of
Agriculture, Technology and Sciences, Naini, Allahabad (U.P.), India
---------------------------------------------------------------------***----------------------------------------------------------------------
Abstract - People opinions and reviews available to us are
one of the most critical factors to decide our views and select
the brand, product or service. Social media has created many
ways for people to raise their voice, beliefs or opinion.
Sentiment Analysis is a case of natural language processing
which could identified the perception of people about any
specific product, service or issue etc. The Sentiment Analysis
tool is to function on a series of expressions for a given item
based on the quality and features. Twitter is a micro-blogging
site where people share their views and thoughts about any
issues or topics, these data shall be useful for sentiment
analysis however presents a challenge for analysis because of
its humongous and disorganized nature. This paper carried
out a study and analysis based on onlinetwitterdata available
in term of people’sopinionsregardingtopengineeringcolleges
in India by developing the Sentiment analyzer. A probabilistic
model classifier such as Decision Tree, K-NN, Naïve-Bayes or
Maximum Entropy shall be used for spelling correction and
classification. In this dissertation Maximum Entropy classifier
has been used to develop the sentiment analyzer.
Key Words: Sentiment Analysis, Machine Learning,
Opinion Mining, Natural Language Processing, Twitter,
Decision Tree, K-NN, Naïve-Bayes & Maximum Entropy.
1. INTRODUCTION
Social Media has got the increased interest among
researchers as it is one of the most significant information
exchange technology. People views on social media to
express their feeling about various products or services,
discussions on current issues, complain and feedbacks for
products which they used in daily life are very informative
and shall be used for-
- Scientific surveys for a social or political purpose.
- Improve product/ services by companies and product
owners.
- Organizations like educational institutions know their
popularity and enhance their quality etc.
Social media such as micro-blogging are very muchinvolved
in everybody’s life now days. People shares their views,
thoughts, information’s etc. across the world free of cost
using only internet connections. Twitter is one such well
known micro-blogging site getting around 500 million
tweets per day [Twitter website]. Each user has a daily limit
of 2,400 tweets and 280 characters per tweet (excluding
Chinese, Japanese, and Korean which have limit of 140
characters). [Twitter website]. Users tweet every day about
various subjects like products, services, day todayactivities,
places, personalities etc.
Twitter is hugely valuable resource from which data can be
extracted in form of useful informations byusingtextmining
tools for sentiment analysis to know the perception of
people (Positive, Negative or Neutral) regarding any topic,
issue or product etc.
2. RELATED WORKS
A lot of works have been put in this area and there is an
immense prevalence and surveys of items and
administrations offered by variousassociations.Mostsimilar
works have been carried out by mainly Nehal Mamgain, Ekta
Mehta, Ankush Mittal & Gaurav Bhatt (2016), Md Shoeb &
Jawed Ahmed, (2017) and Neethu M. S. and Rajasree R.,
(2013). Nehal Mamgain, EktaMehta,AnkushMittal&Gaurav
Bhatt (2016) carried out Sentiment Analysis of Top Colleges
in India Using Twitter Data published in IEEE Transactionon
Computational Techniques in Information and
Communication Technologies. Md Shoeb & Jawed Ahmed,
(2017)also carried out Sentiment AnalysisandClassification
of Tweets Using Data Mining published in IRJET Volume: 04
Issue: 12. Neethu M. S. and Rajasree R., (2013), carried out
Sentiment Analysis in Twitter using Machine Learning
Techniques published in 4th IEEE International Conference
on Computing, Communications and Networking
Technologies, pp. 1-5, Tiruchengode, India. In this work
maximum entropy classifier model has been used for
sentiment analysis which has not been used in these studies.
3. PROCEDURE
To develop the analyzer following procedure or algorithm
has been implemented in system and coding has been done
based on these. These are briefly described in the following
sections.
3.1 Fetching Twitter Data:
In this algorithm, tweets shall be fetched from Twitter using
twitter API, based on keywords i.e. College Names in this
case. Further fetched Tweets shall be stored in the database
for further processing. For fast processing and simple design
provision has been made for maximum 100 nos. of tweets
which shall be fetched for keyword entered and stored in
database.

3.2 Preprocessing:
In this algorithm, the tweets which are foreign made to
database from the twitter API, these tweets comprise of
pointless words, whitespaces, hyperlinks and unique
characters. First we have to do separating process by
evacuating every single superfluous word, whitespaces,
hyperlinks and extraordinary characters. In this study,
twitter data concerning three of the top colleges in India was
obtained in JSON format. Unique tweets referring to IIT-
Kanpur (IIT-K), IIT-Delhi (IIT-D) and IIT-Madras (IIT-M)
were extracted in order to reduce the bias of user opinions,
eliminate redundant data and minimize the frequency of
tweets which may be spam or fake reviews. The tweets also
provide information about the user, location, time-zone. In
order to segregate the user opinion from user information,
preprocessing was performed on the tweets. Removal of
URLs, repeated lettersinsequencewhichoccurredmorethan
twice with two of the same letter, ASCII escapes sequences
for Unicode characters, uninformativesymbolsandsomebut
not all punctuations from thetweets was performed in order
to sustain emoticons in the tweet. Expansion of SMS lingo,
emoticons and abbreviations in net speak has been
performed in order to include user opinions fitted rigidly
under the constraint of 280 characters by referencingaslang
dictionary which contains roughly 5,200 slang words and
incorporates about 270 emoticons. The preprocessing steps
aim to begin the feature extraction process and start
extracting bags of words from the samples. One of the main
focuses is to reduce the final amount of features extracted.
Indeed, features reduction is important in order to improve
the accuracy of the prediction for both topic modeling and
sentiment analysis. Features are used to represent the
samples, and the more the algorithm will be trained for a
specific featurethe moreaccuratetheresultswillbe.Hence,if
two features are similar it is convenient to combine them as
one unique feature. Moreover, if a feature is not relevant for
the analysis, it can be removed from the bag of words.
Lower uppercase letters, thefirst step in thepreprocessingis
to go through all the data and change every uppercase letter
to their corresponding lowercase letter. When processing a
word, the analysis will be case sensitive and theprogramwill
consider “data” and “Data”astwo totally differentwords.Itis
important that, these two words are considered as the same
features. Otherwise, the algorithms will affect sentiments
which may differ to these two words. For example, on these
three sentences: “data are good”, “Awesome data”, and “Bad
Data”. The first and second sentencesbothcontain“data”and
are positive, the third sentence contains “Data” and is
negative. The algorithm will guess that sentences containing
“data” are more likely to be positive and those containing
“Data” negative. If the uppercases had been removed the
algorithm would have been able to guessthatthefactthatthe
sentence contains “data” is not very relevant to detect
whether or the sentence is positive. This preprocessing step
is even more important since the data are retrieved from
Twitter. Social media users are often writing in uppercase
even if it is not required, thus this preprocessing step will
have a better impact on social media data than other
“classical” data.
Remove URLs and user references, Twitter allows user to
include hashtags,userreferencesandURLsintheirmessages.
In most cases, user references and URLs are not relevant for
analyzing the content of a text. Therefore, this preprocessing
step relies on regular expression to find and replace every
URLs by “URL” and user reference by “AT_USER”, this allows
to reduce the total amount of features extracted from the
corpus. The hash tags are not removed since they often
contain a word which is relevant for the analysis, and the “#”
characters will be removed during the tokenization process.
Remove digits, digits are not relevant for analyzing the data,
so they can be removed from the sentences. Furthermore, in
some cases digits will be mixed with words, removing them
may allow to associate two features which may have been
considered different by the algorithm otherwise. For
example, some data may contain “iphone”, when other will
contain “iphone7”. The tokenization process, which will be
introduced later.
Remove stop words, In natural language processing, stop
words are often removed from the sample. These stopwords
are words which are commonly used in a language, and are
not relevant forseveralnaturallanguageprocessingmethods
such as topic modeling and sentiment analysis. Removing
these wordsallowsreducingtheamountoffeaturesextracted
from the samples. As weprocess, each of the tweets,wekeep
adding words to the featurevector and ignoringotherwords.
Let us look at the feature words extracted for the tweets.
Table:1 Positive Feature Words
POSITIVE TWEETS FEATURE WORD
Iit, kanpur, location is very good “ Very Good”,
Iit, delhi, fantastic campus “Fantastic”
Iit, madras, Superb lab “Superb”
Table:2 Neutral Feature Words
NEUTRAL TWEETS FEATURE WORD
Iit, kanpur, college will open on
21.
“ open”,
Iit, delhi, share some picture “picture”
Iit, madras, located in Chennai “located”
Table:3 Negative Feature Words
NEGATIVE TWEETS FEATURE WORD
Iit, kanpur, horrible food in mess “horrible”
Iit, delhi, worst air quality “Worst”
Iit, madras, frequently crime
happens
“crime”
The entire feature vector will be a combination of each of
these feature words. For each tweet, if a feature word is

present, we mark it as 1, else marked as 0. Instead of using
presence/absence off feature word, we may also use the
count of it, but since tweets are just280chars,Iuse0/1.Now,
each tweet shall become as a bunch of 1s and0sandbasedon
this pattern,a tweet is labeledaspositive,neutralornegative.
Given any new tweet, weneed to extract thefeaturewordsas
above and weget one more pattern of 0s and 1sandbasedon
the model learned,theclassifierspredictthetweetsentiment.
It's highly essential to understand this point.
In my full implementation, method of distantsupervisionhas
been used to obtain a large training dataset. This method is
detailed out in Twitter Sentiment ClassificationusingDistant
Supervision.
3.3Self-learningandWordStandardizationSystem:
In this algorithm, first we have to instate the word reference
(first emphasis dictionary). In the lexicon for the most part
we have to introduce the positive, negative nonpartisan and
things. Every single huge datum and information mining
ventures in view of the prepared information, without
preparedinformation(introductionofwords).Soinstatement
of the prepared information is vital. In the self-learning
framework, we are doing word institutionalization, here we
are not considering past, present and future status of the
words, just we are thinking about the word.
3.4 Sentiment Classification:
In this algorithm, preprocessed tweets are brought from the
database one by one. In the first place we require check one
by one watchword whether thatcatchphrase is thing arenot,
if thing we will expel it from the specific tweet. After that the
rest of the watchwords checked with assessment compose,
regardless of whether that catchphrases are certain opinion
or negative conclusion or impartial feeling. The rest of the
watchwords in the tweet which does not has a placewithany
of the supposition will be relegated transitory conclusion in
light of the more check of positive, negative and impartial. In
the second cycle if the remaining word crosses the limit of
positive, negative or nonpartisan, that watchword forever
included as developmentinthelexicon.Atlonglastopinionof
the tweet is recognized in light of the positive, negative and
impartial words in the specific tweet. This part depicts about
the prerequisites. It determines the equipment and
programming prerequisite that are needed for software to
keeping in mind the end goal, to run the application
appropriately. TheSoftwareRequirementSpecification(SRS)
is clarified in point of interest, which incorporates outline of
this exposition and additionally the functional and non-
practical necessity of this dissertation.
Maximum Entropy Classifiers: Average rate at which
information is produced by stochastic source of data is
defined as information entropy. The Max entropy classifier
falls under the category of exponential model. The features
are conditionally independent of each other in Naïve Bayes
classifier but in Max Entropy we do not assume that the
features are conditionally independent of each other. The
Max Entropy selects the one which has the largest entropy
from all the models that fit our training data so it is based on
the principle of Maximum entropy. Different text
classification problems such as language detection, topic
classification, sentiment analysis and more can be solved by
the Max Entropy classifier. Maximum Entropy classifier is
used for classification when we don’t know anything about
the prior distributions andwhenitisunsafetomakeanysuch
assumptions. In classification task when we can not assume
the conditional independence of the features, use of Max
entropy classifier gives best result. In text classification
problems it is better to the use of Max entropy classifier
because in text classification problems features are usually
words which obviously are not independent. Estimation of
parameters of model are called the optimization problem,
due to this Max entropy requires more time to train as
compare to naïve bayes. So this modelgivesrobustresultand
in terms of CPU and memory consumption it is competitive.
Figure:1 Sequence Diagram
TheoreticalBackgroundofMaximumEntropy:TheMaximum
Entropy classifier uses search techniques formaximizingthe

performance of classifier, to find a set of parameters.
Maximum Entropy uses search techniques rather than using
probabilities to set the model’s parameters. It uses the set of
parameters that maximizesthetotallikelihoodofthetraining
corpus, which is defined as
P (Feature) = Σx|in|corpous P (label(x) / features (x)
Where P (label/features) is defined as-
P(label/features)=P(label,features)/ΣlabelP(label,features)
For maximizing the likelihood of training set, there is no way
to directly calculate the model parameters, because of
complex interactions between the effects of related features.
4. RESULTS AND DISCUSSION
From the developed system data fetchedfromtwitter forIIT-
Kanpur, IIT-Delhi and IIT-Madras have been used forfurther
study & analysis. The dynamic tweets fetch from twitter
during the time period of 11 Oct to 20 Oct 2018 on daily
basis i.e. 10 Sets of data have been used for Sentiment
Analysis and for ROC Curve. (*Undecided tweets added in
Neutral which has no impact on analysis). The result shows
that IIT-Kanpur has the most positive sentiment while IIT-
Madras has minimum negative sentiment. The accuracy and
precision of system has been determined by calculating TP,
TN, FP, FN, TPR (Sensitivity), TNR & FPR (1-Specificity) and
based on the same ROC curves have been plotted .
Table:4 Sentiment Analysis Table
Figure:2 Sentiment Analysis Graph Obtained
Figure:3 ROC Curve for IIT-Kanpur
Figure:4 ROC Curve for IIT-Mumbai
Figure:5 ROC Curve for IIT-Delhi

Table:5 Precision and Accuracy Result
5. CONCLUSION
Sentiment Analysis is a very important tool in data mining
which shall utilized huge unstructured informationavailable
online in different social networkingandmicrobloggingsites
for different research or survey. Like for improvement or
betterments of products or services. Today’s many of great
startups and businesses are the results of opinion mining. In
this work one of the analysis has been performed by
developing the analyzer for college sentimentanalysiswhich
may also be helpful for otherfields withsomemodificationin
algorithm & coding. However sentiment selection,
classification and calculations are as yet an open research
field. Most of the classificationsusedinsentimentanalysisfor
giving best result are Decision Tree, K-NN and NaïveBayes,
Maximum Entropy, and Support Vector Machines. In which
Maximum Entropy method has been selected for this work
which shall be considered as one of the best classifier.
6. FUTURE SCOPE OF WORK
While this study and work it has been observed that the
neutral counts of tweets have not been taken care in account
which might be give somestrength to the sentimentanalysis.
Also as a trend it has been extended to globalists in all fields
so tweets in other foreign languages may also be taken care
and in India it shall be extended for Hindi and other regional
languages also. One more characteristic which may open a
door in this research area is to consider opposition views or
tweets wisely and segregatetheseaccordingly.Likeifwetake
views of users on government polices by online sentiment
analysis, opposition parties always prone to opposethese.So
while online sentiment analysis views or tweets of these
institutions and persons shallbesuitablyanalyzethathowto
tackle with these and make a framework for such type of
sentiment analysis.
7. ACKNOWLEDGEMENT
I express profound sense of gratitude to my supervisor, Dr.
Tulika, Department of Computer Science and Information
Technology, Sam Higginbottom University of Agriculture,
Technology and Sciences, Naini, Allahabad, for her
motivation, feedbackonresearchquestions,experimentsand
the writing and presentations that followed, philosophical
discussions, and the wisdom conveyed. I show herewith
appreciation for her continuous help and support to Dr.
Tulika, a great mentor, for her patience and enthusiasm, and
It is due to her excellent brilliant ideas, able guidance and
directions that I could growasaM.Tech.scholarandproduce
this work. I heartily thank Dr. Tulika for giving me adequate
of time to educate me in and developing the idea. I am
thankful to HOD (CS&IT) & SAC members for their valuable
observation on this work and their guidance to make this
success. I am also thankful to and fortunate enough to get
constant encouragement, support and guidance from all
Teaching staffs of CS&IT which helped me in successfully
completing this work.
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